Gene regulation is a central topic in cell biology. Advances in omics technologies and the
accumulation of omics data have provided better opportunities for gene regulation studies …

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to
provide an insight into a number of cellular phenotypes from the genomic level and reveal …

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Technological advances in genomics and imaging have led to an explosion of molecular
and cellular profiling data from large numbers of samples. This rapid increase in biological …

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class
of methods, based on artificial neural networks, quickly became popular due to its …

High-throughput sequencing, large-scale data generation projects, and web-based cloud
computing are changing how computational biology is performed, who performs it, and what …

Extracting inherent valuable knowledge from omics big data remains as a daunting problem
in bioinformatics and computational biology. Deep learning, as an emerging branch from …

Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to
design software for deep-learning analyses in regulatory genomics that are findable …

Deep neural networks and support vector machines have been shown to accurately predict
genome-wide signals of regulatory activity from raw DNA sequences. These models are …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …